Rolling Bearing Fault Diagnosis Based on CEEMD and Time Series Modeling

Abstract

Accurately identifying faults in rolling bearing systems by analyzing vibration signals, which are often nonstationary, is challenging. To address this issue, a new approach based on complementary ensemble empirical mode decomposition (CEEMD) and time series modeling is proposed in this paper. This approach seeks to identify faults appearing in a rolling bearing system using proper autoregressive (AR) model established from the nonstationary vibration signal. First, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of intrinsic mode functions (IMFs) by means of the CEEMD method. Second, vibration signals are filtered with calculated filtering parameters. Third, the IMF which is closely correlated to the filtered signal is selected according to the correlation coefficient between the filtered signal and each IMF, and then the AR model of the selected IMF is established. Subsequently, the AR model parameters are considered as the input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of a rolling bearing. Experimental study performed on a bearing test system has shown that the presented approach can accurately identify faults in rolling bearings.

Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method

“…1 The non-linear and non-stationary characteristics of the signal and the interference of external factors on the obtained vibration signal are all factors that affect the extraction of features from complex vibration signals. [2][3][4][5] For this purpose, a large number of articles on feature extraction methods can be found in the literature. As a classical time-frequency analysis method, wavelet transform (WT) is applied to mechanical fault diagnosis for its multi-scale analysis features, high time-frequency resolution, and rigorous mathematical foundation.…”

A Frequency-Weighted Energy Operator and complementary ensemble empirical mode decomposition for bearing fault detection

“…The idea adopted in [5] and [17] is to consider the IMFs which have a poor correlation with the original signal as irrelevant ones and assume a threshold that can be set to discriminate between relevant and irrelevant IMFs. Other techniques based on energy and/or correlation measures were developed for identifying the most representative IMFs after the EMD process [18][19][20][21]. Moreover, Intrinsic Mode Entropy (IMEn) was developed to measure entropy over accumulative sums of IMFs obtained by the EMD [22].…”

A fault diagnosis method for a practical engineering application based on CEEMD and ELM

“…However, the more effective ensemble strategy makes CEEMD can not only overcomes mode mixing of EMD, but also avoid low computing efficiency of EEMD [10,11]. And CEEMD is able to fully grasp the main features of the original signal.…”